Dynamic Incentive Mechanism Design for COVID-19 Social Distancing


  • Xuan Rong Zane Ho Nanyang Technological University
  • Wei Yang Bryan Lim Alibaba-NTU Joint Research Institute
  • Hongchao Jiang Alibaba-NTU Joint Research Institute
  • Jer Shyuan Ng Alibaba-NTU Joint Research Institute
  • Han Yu Nanyang Technological University
  • Zehui Xiong Singapore University of Technology and Design
  • Dusit Niyato Nanyang Technological University
  • Chunyan Miao Nanyang Technological University Alibaba-NTU Joint Research Institute




Incentive Mechanism, Crowdsourcing, Crowd Counting


As countries enter the endemic phase of COVID-19, people's risk of exposure to the virus is greater than ever. There is a need to make more informed decisions in our daily lives on avoiding crowded places. Crowd monitoring systems typically require costly infrastructure. We propose a crowd-sourced crowd monitoring platform which leverages user inputs to generate crowd counts and forecast location crowdedness. A key challenge for crowd-sourcing is a lack of incentive for users to contribute. We propose a Reinforcement Learning based dynamic incentive mechanism to optimally allocate rewards to encourage user participation.




How to Cite

Ho, X. R. Z., Lim, W. Y. B., Jiang, H., Ng, J. S., Yu, H., Xiong, Z., Niyato, D., & Miao, C. (2022). Dynamic Incentive Mechanism Design for COVID-19 Social Distancing. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13173-13175. https://doi.org/10.1609/aaai.v36i11.21718